Social media profiling for one or more authors using one or more social media platforms

ABSTRACT

A system is presented that profiles authors and social media data across different media platforms and is capable of determining the author&#39;s overall social impact. In one aspect, this is accomplished by using a data retrieval service to trawl various web-sites and social media platforms for information about authors which can then be associated with those authors in a profile database. In one example, an author may post an entry on his/her blog and the data retrieval service can access the profile information of the author, on the blog, where various aspects of the profile information (e.g., real name, employee information, home address) can be matched with candidates in a profile database. From the information gathered, authors can be linked across multiple, different platforms, and an overall social impact of each of the authors can be determined.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/465,335 filed May 7, 2012.

BACKGROUND

The Internet and social media platforms (e.g., Facebook®, Twitter®,blogs) provide authors with an easy-to-use interface for conveyinginformation and opinions. Authors can log-on to these platforms fromtheir personal computers, cell phones, or other communication devicesand convey information available to the world within seconds.

Many authors convey information across multiple platforms. For example,an individual may have a Twitter® account, a Facebook® account, and ablog for conveying information. Thus, a author may post an opinion onFacebook® using his/her Facebook® account and then post a similar orrelated opinion on his/her blog.

Sentiment analysis technology takes advantage of these media andplatforms and uses sophisticated tools for analyzing the author data forparticular “sentiment” (the term sentiment can refer to an attitude,opinion, and/or emotion towards a particular topic). For example, aauthor may post on a blog their fondness of the new Apple iPhone®. Theycould likewise log into their Twitter® account and post a similaropinion. Sentiment analysis extracts this data from the various socialmedia platforms and analyzes it to determine information about theauthor and associate the author and his/her opinion with a particularsentiment. However, when the author posts opinions on a topic usingmultiple, different social media platforms, it is difficult toadequately link the author across platforms and determine the author'soverall social impact in the world. This is especially true when theauthor's identity is not as apparent on a particular platform. Forexample, an author may use his/her real name when posting entries onFacebook but may use a pseudonym when posting entries on his/her blog.Thus, it would be advantageous to profile the authors on the differentsocial media platforms and automatically link the authors across themultiple, different platforms to determine their overall social impact.

SUMMARY OF THE TECHNOLOGY

A system is presented that profiles authors and social media data acrossdifferent media platforms and is capable of determining the author'soverall social impact. In one aspect, this is accomplished by using adata retrieval service to trawl various web-sites and social mediaplatforms for information about authors which can then be associatedwith those authors in a profile database. In one example, an author maypost an entry (or a composition) on his/her blog and the data retrievalservice can access the profile information of the author, on the blog,where various aspects of the profile information (e.g., real name,employee information, home address) can be matched with candidates in aprofile database. From the information gathered, authors can be linkedacross multiple, different platforms, and an overall social impact ofeach of the authors can be determined.

A method for analyzing and evaluating social media data, to determine asocial impact of author comments on one or more topics, using aninformation processing apparatus having one or more processors ispresented. The method comprises determining a first sentiment on a firstcomposition on a topic composed by an author using a first social mediadevice, determining a second sentiment on a second composition on arelated topic by an author using a second social media device,determining whether the author using the first social media device isthe same author as the author using the second social media device,comparing the first sentiment of the author of the first compositionwith the second sentiment of the author of the second composition basedon whether the author using the first social media device is the sameauthor as the author using the second social media device, scoring, viathe one or more processors, the first sentiment of the author of thefirst composition based on the comparison between the first sentimentand the second sentiment, and determining a social impact of the authorbased on the scored sentiment.

A non-transitory computer-readable storage medium having computerreadable code embodied therein which, when executed by a computer havingone or more processors, performs the method for analyzing social mediadata of the preceding paragraph.

The technology also relates to an information processing apparatushaving a memory configured to store social media data and one or moreprocessors, coupled to the memory, configured to analyze and evaluatesocial media data to determine a social impact of author comments on oneor more topics. The one or more processors in the information processingapparatus are further configure to determine a first sentiment on afirst composition on a topic composed by an author using a first socialmedia device, determine a second sentiment on a second composition on arelated topic by an author using a second social media device, determinewhether the author using the first social media device is the sameauthor as the author using the second social media device, compare thefirst sentiment of the author of the first composition with the secondsentiment of the author of the second composition based on whether theauthor using the first social media device is the same author as theauthor using the second social media device, score the first sentimentof the author of the first composition based on the comparison betweenthe first sentiment and the second sentiment, and determine a socialimpact of the author based on the scored sentiment.

The technology also relates to an information processing system havingone or more social media devices and an information processingapparatus. The one or more social media devices having a memoryconfigured to store social media data, one or more processors configuredto process social media data, and a transceiver configured totransmit/receive social media data. The information processing apparatushaving a memory configured to store social media data, a transceiverconfigured to transmit/receive social media data, and one or moreprocessors, coupled to the memory, configured to analyze and evaluatesocial media data to determine a social impact of author comments on oneor more topics. The one or more processors in the information processingapparatus are further configured to determine a first sentiment on afirst composition on a topic composed by an author using a first socialmedia device, determine a second sentiment on a second composition on arelated topic by an author using a second social media device, determinewhether the author using the first social media device is the sameauthor as the author using the second social media device, compare thefirst sentiment of the author of the first composition with the secondsentiment of the author of the second composition based on whether theauthor using the first social media device is the same author as theauthor using the second social media device, score the first sentimentof the author of the first composition based on the comparison betweenthe first sentiment and the second sentiment, and determine a socialimpact of the author based on the scored sentiment.

In a non-limiting, example implementation a first profile of the firstauthor on the first social media device is accessed, information aboutthe first author is collected based on the first profile, a secondprofile of the second author on the second social media device isaccessed, information about the second author is collected based thesecond profile, the collected information based on the first profile iscompared with the collected information based on the second profile todetermine if the first author is the same author as the second author,and scored sentiment of the first author and the second author areaggregated to produce an overall sentiment thereby determining thesocial impact of the first and second author.

In another non-limiting, example implementation the first and secondprofile includes at least one of a username of the authors, an age ofthe authors, a gender of the authors, a household income of the authors,career information of the authors, a location of the authors, a legalname of the authors, a pseudonym of the authors, and/or an ethnicity ofthe authors.

In yet another non-limiting, example implementation the first and secondprofile includes at least one of a username of the authors, an age ofthe authors, a gender of the authors, a household income of the authors,career information of the authors, a location of the authors, a legalname of the authors, a pseudonym of the authors, and/or an ethnicity ofthe authors.

In another non-limiting, example implementation the social media devicecomprises at least one of publications, social media websites, forums,blogs, radio broadcasts, and/or television broadcasts.

In yet another non-limiting, example implementation the first sentimentof the first author relates to a positive, negative, or neutralsentiment of the first author of the first composition, the first socialmedia device is different than the second social media device, and therelated topic is the same topic.

In another non-limiting, example implementation a higher score is givento the first sentiment of the first author when the first sentiment ofthe first author is opposite to the second sentiment of the secondauthor on the second composition of the related topic.

In yet another non-limiting, example implementation a higher score isgiven to the first sentiment of the first author when the firstsentiment of the first author is the same as the second sentiment of thesecond author on the second composition of the related topic.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example embodiment of a social media profilingsystem;

FIG. 2 is a block diagram of an example embodiment of a social mediaprofiling apparatus interacting with one or more social media sources;

FIG. 3 is a block diagram of an example embodiment of one or morespiders retrieving data from one or more social media sources;

FIG. 4 is a block diagram of an example embodiment of one or morespiders interacting with a profiler and one or more databases;

FIG. 5 is an example application flowchart showing a flow of processesfor a social profiling system; and

FIG. 6 is an example application flowchart showing a more detailed flowof processes for matching authors.

DETAILED DESCRIPTION OF THE TECHNOLOGY

In the following description, for purposes of explanation andnon-limitation, specific details are set forth, such as particularnodes, functional entities, techniques, protocols, standards, etc. inorder to provide an understanding of the described technology. It willbe apparent to one skilled in the art that other embodiments may bepracticed apart from the specific details described below. In otherinstances, detailed descriptions of well-known methods, devices,techniques, etc. are omitted so as not to obscure the description withunnecessary detail. Individual function blocks are shown in the figures.Those skilled in the art will appreciate that the functions of thoseblocks may be implemented using individual hardware circuits, usingsoftware programs and data in conjunction with a suitably programmedmicroprocessor or general purpose computer, using applications specificintegrated circuitry (ASIC), and/or using one or more digital signalprocessors (DSPs). The software program instructions and data may bestored on computer-readable storage medium and when the instructions areexecuted by a computer or other suitable processor control, the computeror processor performs the functions. Although databases may be depictedas tables below, other formats (including relational databases,object-based models and/or distributed databases) may be used to storeand manipulate data. Also, any reference to the term “non-transitory” isintended only to exclude subject matter of a transitory signal per se.The term “non-transitory” is not intended to exclude computer readablemedia such as volatile memory (e.g. random access memory or RAM) orother forms of storage that are not excluded subject matter.

Although process steps, algorithms or the like may be described orclaimed in a particular sequential order, such processes may beconfigured to work in different orders. In other words, any sequence ororder of steps that may be explicitly described or claimed does notnecessarily indicate a requirement that the steps be performed in thatorder. The steps of processes described herein may be performed in anyorder possible. Further, some steps may be performed simultaneouslydespite being described or implied as occurring non-simultaneously(e.g., because one step is described after the other step).

Moreover, the illustration of a process by its depiction in a drawingdoes not imply that the illustrated process is exclusive of othervariations and modifications thereto, does not imply that theillustrated process or any of its steps are necessary, and does notimply that the illustrated process is preferred. The apparatus thatperforms the process may include, e.g., a processor and those inputdevices and output devices that are appropriate to perform the process.

Various forms of computer readable media may be involved in carryingdata (e.g., sequences of instructions) to a processor. For example, datamay be (i) delivered from RAM to a processor; (ii) carried over any typeof transmission medium (e.g., wire, wireless, optical, etc.); (iii)formatted and/or transmitted according to numerous formats, standards orprotocols, such as Ethernet (or IEEE 802.3), SAP, ATP, Bluetooth, andTCP/IP, TDMA, CDMA, 3G, etc.; and/or (iv) encrypted to ensure privacy orprevent fraud in any of a variety of ways well known in the art.

The technology described herein is directed to a social media profilingsystem that profiles authors (also referred to herein as “users”) thatuse various social media platforms. Such profiling is useful to clientsthat provide services, sell products, etc. In an example embodiment, aset of web crawling services trawl the World Wide Web for user accountsfrom popular social networking websites and other Internet basedservices. It should be appreciated that the term “trawl” can generallyrefer to accessing/sifting through large volumes of data, archives,and/or looking for something of interest.

From information collected in the search, commonalities such as sharedusername, or links to other author profiles are used to build a morecomprehensive understanding of the author, the size of the author'ssocial circles and ultimately, the author's potential social value tothe client. From this information gathered, a client can determinewhether an author posting a positive or negative comment, article, etc.related to one or more of the client's products or services mightinfluence the general public, for example.

One illustrative example uses a comedian, who has a dominant followingon Twitter® and Facebook®. Assume the comedian is an Apple® fan, andgenerally posts positive reviews of Apple® products. His comments mightreach millions of followers, who may be influence by his posts to seekproducts and/or services from Apple®.

A web crawler service can be used to target a specific form of socialcommunity on the World Wide Web. One example is a social focal pointlike Twitter® or Facebook®, or other Internet based services such asvBulletin forums, or WordPress blogs.

The web crawler can visit the target website to detect “mentions” ofusernames (or a profile page). If a profile page is found, then thecrawler collects all public information about the target author forstorage in a database. The web crawler also attempts to identifypotential links to other author profiles that belong to the targetauthor, which allows the system to link different author profiles fromdifferent sites together to build a more comprehensive picture of thetarget author. Information such as the amount of posts and the number offriends the author has is recorded to help determine the target author'slikely social value on the website.

Web crawlers can be customized to target specific sites and products.For example, if a crawler designed to crawl over the Twitter® websitemay only be able to crawl Twitter®, then a separate crawler would beneeded to crawl Facebook®. However, a crawler built to crawl vBulletinforums may be configured to crawl multiple websites implementingvBulletin forums. Some of the information that may be obtained fromcrawling may include, but is not limited to, user post data and/or userbackground data.

The content retrieved by the web crawlers can be used to populate one ormore databases. Once the content is retrieved, the system can thenattempt to analyze records, for one or more relations to other collectedauthor profiles. This can be done based on username similarity or mutuallinks mentioned in profiles, for example.

With data collected from the web crawlers, additional crawlers can bedeployed to periodically revisit and update the information collected onthe authors. This allows the system to maintain current data on targetauthors, and also allows for the identification of additional details,like the rate of posting, the trend in friends (if the author is gainingor losing audience), and depending on the target website/product,possibly information on the topics that the author is interested in.

With information gathered on authors, one or more weights can beassessed to a post by the author based on the topics the author postsabout, their likely influence over their followers, and the volume ofaudience. An author may also post differently for different companies.Using the example above, if the author favors Apple®, a negative postabout Apple® from the author may be more negatively weighted given thatthis author's reviews are normally positive for Apple®. A greaterweighting may also be provided when a critic of a company or productfavors, for example, the latest product/move from the company.

It should be appreciated that the system is not limited to profilingsocial media platforms and can profile all forms of media including, butnot limited to, social media, print, online web and broadcast data. Itshould also be appreciated that that the social impact is not onlylinked to sentiment and can also take into account details of thecontent and text written by an author to determine the social impact ofthe author and whether or not it is the same author. By doing this,several pieces of information can be captured across media typesincluding topic, sentiment, author name, and “spidered” information fromonline journalist contact sites to make the comparison and find a match.

FIG. 1 is a diagram of an example embodiment of a social media profilingsystem. A web crawler, or Spider, is deployed to search various socialmedia web-sites (e.g., Facebook®, Twitter®) and/or blogs and retrieveauthor posts and information regarding the author. The Spider can employa URL pool to direct the Spider to various URLs in order to retrieveposts and author profile information. That is, the URL pool can beconfigured as a list of URLs containing author information. This allowsthe Spider to focus just on sites that will return the most valuablejournalist data.

Upon retrieving the profile information using the Spider, theinformation can be processed by a profile determiner which willdetermine if there is a match of the profile in the profile database DB.As explained further below, if no match is found, a new profile iscreated, and if a match is found, the profile is linked with the profilein the profile database DB. A user interface can also be provided togenerate reports and/or provide information via a website, for example,to show the author's overall social impact.

Applications (APP1, APP2) can also be used by the system to make use ofthe journalist information. For example, one application could be aPress Relations platform which needs journalist contact information todistribute information to/from. If the information is comprehensive, itallows for target email distribution of corporate information. Anotherapplication could be a media monitoring application that may require thedata to provide valuable information on a journalist for a user who isanalyzing press mentions on an organization.

FIG. 2 shows a block diagram of an example embodiment of a social mediaprofiling apparatus interacting with one or more social media sources.In FIG. 2, a social media profiling apparatus 100 can be configured tohave a CPU 101, a memory 102, and a data transmission device DTD 103.The DTD 103 can be, for example, a network interface device that canconnect the social media profiling apparatus 100 to one or more socialmedia sources 200 a-n. The connection can be wired, optical, or wirelessand can connect over a Wi-Fi network, the Internet, or a cellular dataservice, for example. The DTD 103 can also be an input/output devicethat allows the apparatus 100 to place the data on a computer-readablestorage medium. It should be appreciated that the data transmissiondevice 103 is capable of sending and receiving data (i.e. atransceiver).

The social media profiling apparatus 100 is also configured to have oneor more spiders 104, profilers 105, and profile databases DB 106. Asexplained further below, the spiders 104 are configured to trawl thevarious social media sources 200 a-n in order to obtain information onauthors using the sources 200 a-n. The spiders 104 can accessinformation from the sources 200 a-n via a network, such as theInternet, and can be configured to access the sources 200 a-n using theDTD 103.

FIG. 3 is a block diagram of an example embodiment of one or morespiders retrieving data from one or more social media sources. Asexplained above, the apparatus 100 can be configured to have one or morespiders 104 a-n that trawl the social media sources 200 a-n for data. Itshould be appreciated that each social media source 200 a-n may have aCPU 201, a memory 202, and a DTD 203. Much like the DTD 103, the DTD 203can be, for example, a network interface device that can connect thesocial media sources 200 a-n to the social media profiling apparatus100. The connection can be wired, optical, or wireless and can connectover a Wi-Fi network, the Internet, or a cellular data service, forexample. The DTD 203 can also be an input/output device that allows thesources 200 a-n to place the data on a computer-readable storage medium.It should be appreciated that the data transmission device 203 iscapable of sending and receiving data (i.e. a transceiver).

In an example embodiment, each social media source 200 a-n can also beconfigured to have social media data 204 a-n and/or a social mediaprofile 205 a-n. The social media data 204 a-n can be, for example, anauthor post, such as a comment on Facebook® or can be a blog entry. Inan example embodiment, the social media 204 a-n will be an author postthat is commenting on a particular topic and has an author associatedwith the post. In an example embodiment, the social media profile 205a-n can be a profile of the author for the post. For example, Facebook®may have an author profile associated with the author of the particularpost. The author profile information can be stored in the social mediaprofile 205 a-n where a spider 104 a-n can access both the social mediadata 204 a-n and the social media profile 205 a-n associated with thedata 204 a-n.

Using the example from above, an author may have an account withFacebook®. With this account, the author may have various backgroundinformation stored in his/her profile on Facebook®. For example, theauthor's gender, age, ethnicity, location of birth, present location,employer, and/or full legal name (among many other segments ofinformation related to the author's background) may be associated withthe author's account. The very same comedian may also have a Twitter®account where he posts information. Likewise, his Twitter® account willalso have background information stored in his profile. By having accessto the profile accounts for Facebook® and Twitter®, the backgroundinformation can be analyzed to attempt to determine a link betweenauthors. Thus, such a system is advantageous where it may not beapparent to a sentiment analysis system that two separate accounts ondifferent social media platforms are for the very same individual. Thatis, the sentiment analysis system may link the profiles of authors andperform sentiment analysis taking into account the identity of theauthor. From there, an overall social impact of a single author can bedetermined taking into account the different mediums in which the authorconveys information.

FIG. 4 shows a diagram of one or more spiders 104 a-n interacting with aprofiler 105 and one or more databases 106 a-n in the apparatus 100. Asexplained above, the spiders 104 a-n are deployed to trawl web-sites andsocial media platforms to gather both author posts and information aboutthe authors. Upon retrieving the author post data and the author profiledata, a profiler 105 compares the data with respect to data stored inone or more databases 106 a-n. As can be seen in FIG. 4, the databases106 a-n store both the social media data 106 a-1-106 n-1 and the socialmedia profile data 106 a-2-106 n-2.

The profiler 105 can be configured to match the data retrieved from thespiders 104 a-n with data stored in the databases 106 a-n. Using theexample from above, a comedian may have a Facebook® account where hemakes several posts daily. This data may be previously stored in thedatabases 106 a-n where both the author posts and the profileinformation of the author are stored in the databases, respectively. Asmentioned above, the very same comedian may decide to open a Twitter®account where the Twitter® account may have an author name that is notat all similar to the user name on Facebook®. In this example,information related to the author's Facebook® account as well as theauthor posts may be stored in one or more databases 106 a-n whereinformation from Twitter® may not have yet populated the databases 106a-n.

Thus, when the spiders 104 a-n acquire the author post data/compositionsand the profile information from Twitter®, the profiler 105 can comparethe background information of the author on Twitter® to backgroundinformation of authors stored in the one or more databases 106 a-n. Uponfinding a successful match, the apparatus 100 may then associate thecomedian's Twitter® posts with his Facebook® posts, thus providing amore robust sentiment analysis of the author posts as the apparatus 100has the ability to analyze social media data from various differentsocial media platforms and associate the data with a single author. Itshould be appreciated that the data from various accounts (e.g.,Twitter®, Facebook®) may already be stored in the one or more databases106 a-n and the profiler 105 can still link this data in the same manneras it would as though it were processing the data from the spiders 104a-n. Upon linking the author across different accounts, a single authoridentity exists in which that author's overall social impact can bedetermined.

FIG. 5 shows an application flowchart for a flow of processes for asocial profiling system. The process begins when a query is generateddirecting a spider both where and for what to search (S5-1). The spideris deployed to a social media source (e.g., Twitter®) where variousinformation is accessed from the source based on one or more criteriaset out in the query (S5-2). The spider can retrieve social media data,such as an author post on Facebook® or Twitter® or a blog entry on aweb-site as well as access and retrieve information from an authorprofile on the social media source (S5-3). That is, the spider canretrieve information such as the author name as well as the author'sfull legal name, gender, ethnicity, date of birth, place of birth,current residence, etc.

The extracted information can be used to populate information in one ormore databases (S5-4). From there, the information can be compared toother profile information in the one or more databases (S5-5), furtherdetails of which will be discussed with respect to FIG. 6.

If there is no match (S5-6) between the profile information received bythe spiders and profile information in the one or more databases, aprofile can be created (S5-7) and stored in the one or more databasesfor future analysis. An initial sentiment will then be performed withrespect to the newly created profile (S5-8).

If a match is found (S5-6), then the profile will be linked with aprofile in the one or more databases (S5-9). Thus, a single author willbe associated with social media data spanning multiple, different socialmedia platforms. From there, sentiment can be compared to and analyzedwith respect to sentiment data previously stored in the one or moredatabases (S5-10). So for example, if an author normally posts positivereviews about products from a particular company on Facebook® and theauthor makes a generally negative comment about the company on Twitter®,the analysis will not be performed in a vacuum and will take intoaccount previous author posts on Facebook®. Thus, the sentiment analysiswill be generated in view of the author's already established sentimenton the other, different social media platforms and an overall socialimpact of the author will be determined (S5-11).

In generating the sentiment value (S5-10), sentiment of the author canbe scored taking into account the social impact of the author acrossmultiple, different platforms. The scoring of the author can beaccomplished through the assignment of a numerical value ranging from −1to 1, for example, to indicate the sentiment where −1 is a negativesentiment and +1 is a positive sentiment. So, compositions of the authormay be scored as discrete arithmetical sums.

Using the example above, an author may have many posts/compositions onFacebook® related to Apple® products where a positive (+1) sentiment hasbeen assigned to the post/composition. This value can be aggregated andassociated with the author. So when the same author makes apost/composition on his Twitter® account that is generally negative(−1), this value can be aggregated with the author's already establishedsentiment via multiple Facebook® posts. Thus, the author can have anaggregated sentiment associated with his posts about Apple® productsacross multiple, different social media platforms. This aggregatedsentiment thus helps determine a single, overall social impact of theauthor. Also, if a subsequent sentiment is derived from the same authorfor subsequent compositions relating to the original composition (i.e.,comments and additional material relating to the original composition),these will be summed to provide an aggregated score for all of thecompositions relating to the original composition in a group ofcompositions.

FIG. 6 shows an example application flowchart for various analysis thatcan be performed on the profile data with respect to profile data storedin the one or more databases. It should be appreciated that the analysisshown in FIG. 6 is by way of non-limiting example and other varioustypes of analysis may be performed. The profiles are accessed in the oneor more databases (S6-1) where various analysis and matching isperformed on different aspects of the profile information. A firstanalysis could be to analyze two user names (S6-2). For example, a username of John Smith on Facebook® would match exactly with a John Smith onTwitter®. Of course, a user name of jsmith on Twitter® may also be acandidate for a match as it indicates a first initial and last namematching the user name on Facebook®. It should be appreciated that justbecause a user name may match either exactly or through some equivalent,that does not necessarily mean the profiles will match. That is, in thisexample, the individual John Smith on Facebook® may be an entirelydifferent individual than John Smith on Twitter®.

Analysis can also be performed on the author's full legal name with eachaccount (S6-3). Using the example above, the author having the authorname John Smith on Facebook® may legally be named John Ryan Smith wherethe author John Smith on Twitter® may legally be named John MichalSmith. Thus, in this example, the John Smith from Twitter® would notmatch with the John Smith from Facebook®.

Analysis can be further performed using a possible pseudonym of theauthor (S6-4). In the example where the author may be a relativelyfamous author, the author may decide to publish certain informationunder a pseudonym. Thus, a pseudonym associated with the user accountsmay be linked to each other as well.

Various information related to demographics may also be analyzed for amatch (S6-5). In the example above, John Smith may have identical legalnames under both the Facebook® and Twitter® account but still may not bethe same John Smith. After analyzing demographic information such as,but not limited to, gender, race, age, disabilities, mobility, homeownership, employment status, and location, the determination of whetherthey are the same John Smith can be better decided. For example, theJohn Smith on Facebook® may be a Caucasian male of age 35 and living inAustin, Tex. where the John Smith on Twitter® may be a Caucasian male ofage 35 and living in Chicago, Ill. Such a scenario may produce less of alikelihood that they are not the same John Smith. Of course, otherinformation should be analyzed as well as the profiles of John Smith maynot be entirely updated. That is, John Smith may have lived in Chicago,Ill. but just did not update his profile on Twitter® as he may now beliving in Austin, Tex.

Employment information may also be analyzed to determine if there is amatch between profiles (S6-6). For example, the employer name, length ofemployment, title of the individual at the particular organization inwhich the individual is employed, or the location of the employer mayall be analyzed to determine if there is a match. So once again, JohnSmith of Facebook® may be employed with Microsoft® in California whereJohn Smith of Twitter® may work at the U.S. Patent and Trademark Officein Alexandria, Va.

Once all of the information has been compared and analyzed (S6-7), thevarious factors that are alike can be weighed against the variousfactors that are dissimilar and the determination of a match can then bemade (56-8). If no match is found (S6-9), a NO MATCH FLAG is set and theprocess ends where if a match is found (S6-10) a MATCH FLAG is set andthe process also ends.

While the technology has been described in connection with what ispresently considered to be practical and preferred embodiments, it is tobe understood that the technology is not to be limited to the disclosedembodiments, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

1. A method for analyzing and evaluating social media data, to determinea social impact of author comments on one or more topics, using aninformation processing apparatus having one or more processors, themethod comprising: determining a first sentiment on a first compositionon a topic composed by an author using a first social media platform;determining a second sentiment on a second composition on a relatedtopic by an author using a second social media platform; determiningwhether the author using the first social media platform is the sameauthor as the author using the second social media platform; comparingthe first sentiment of the author of the first composition with thesecond sentiment of the author of the second composition when the authorusing the first social media platform is the same author as the authorusing the second social media platform; scoring, via the one or moreprocessors, the first sentiment of the author of the first compositionbased on the comparison between the first sentiment and the secondsentiment; and determining a social impact of the author based on thescored sentiment.